Recent advancements in quantum computing have opened new avenues for tackling<br>long-standing complex combinatorial optimization problems that are intractable<br>for classical computers. Predicting secondary structure of mRNA is one such<br>notoriously difficult problem that can benefit from the ever-increasing<br>maturity of quantum computing technology. Accurate prediction of mRNA secondary<br>structure is critical in designing RNA-based therapeutics as it dictates<br>various steps of an mRNA life cycle, including transcription, translation, and<br>decay. The current generation of quantum computers have reached utility-scale,<br>allowing us to explore relatively large problem sizes. In this paper, we<br>examine the feasibility of solving mRNA secondary structures on a quantum<br>computer with sequence length up to 60 nucleotides representing problems in the<br>qubit range of 10 to 80. We use Conditional Value at Risk (CVaR)-based VQE<br>algorithm to solve the optimization problems, originating from the mRNA<br>structure prediction problem, on the IBM Eagle and Heron quantum processors. To<br>our encouragement, even with ``minimal'' error mitigation and fixed-depth<br>circuits, our hardware runs yield accurate predictions of minimum free energy<br>(MFE) structures that match the results of the classical solver CPLEX. Our<br>results provide sufficient evidence for the viability of solving mRNA structure<br>prediction problems on a quantum computer and motivate continued research in<br>this direction.